Why Real Estate Needs Purpose-Built AI Over Yet Another Chatbot

Real estate is the world’s largest asset class ($379 trillion). In the United States alone, the commercial real estate market value is estimated to be $25.8 trillion, yet much of the sector still relies on manual document review. General-purpose legal AI tools command valuations in the billions. While these tools excel at contract review and legal research, they struggle with the unique spatial and regulatory complexities of real estate. Although GPTs have taken the lion’s share of both AI funding and media attention, the world’s largest asset class requires AI tools specifically built for it.  

Property is a visual craft 

As a former shareholder at an AM Law 100 firm, Polsinelli, and now Orbital’s Head of US Legal, I see this challenge daily. General-purpose AI lacks the visual understanding that underpins real estate transactions. Every deal revolves around maps, blueprints, and spatial data. Lawyers and developers must determine exact property boundaries, analyse overlay plans for construction discrepancies, and process visual data to define legal rights, such as easements and rights-of-way.  

A chatbot may summarise leases but it cannot, for instance, visualise parcels and easements on interactive maps or produce client-ready drafts aligned to your real estate team’s exact tone and structure. 

These tasks form the backbone of real estate due diligence. They appear repeatedly on lawyers’ desks, demanding automation. Yet general AI tools require such extensive prompt customisation that real estate professionals find them impractical and slow to implement.  

Purpose-built AI understands the property sector visually from the start. These assertions about the benefits of purpose-led AI tools are based on the value real estate-specific AI brings to their team, helping them to work more efficiently. The bottom line in terms of value-add for these teams is that the tools can help them to manage the flow of high-volume and complex real estate matters with greater consistency. 

The regulatory maze 

Real estate operates within thousands of distinct regulatory frameworks across states and municipalities. Each jurisdiction maintains unique zoning laws and compliance requirements that change frequently.  

Take the US market as the most potent example of all of this coming together. A property lawyer handling a national portfolio in the US must navigate federal regulations, state-level laws, and local ordinances simultaneously. New York City’s intricate zoning code spans multiple use districts, floor area ratios, and variance procedures. Houston operates with virtually no zoning at all, relying instead on deed restrictions. These cities are governed by completely different regulatory philosophies, timescales, and decision criteria.

These nuances matter because an overlooked local ordinance can kill a deal worth millions. Real estate professionals need AI that understands jurisdictional complexity from day one, not general-purpose tools that require extensive customisation for each framework.  Purpose-built AI embeds this regulatory intelligence at deployment. 

The cost of “good enough” AI in real estate 

Despite widespread testing, a recent study highlighted that 30% of lawyers actively use AI due to a glaring trust deficit. In real estate, a single misread easement or boundary error can derail multi-million-dollar transactions. When stakes run this high, “good enough” becomes dangerously inadequate. Trust emerges through accountability and proven accuracy. 

The market now sees insurance-backed AI outputs emerging as a much-needed trust mechanism. This coverage transforms AI from an experimental tool into a trusted professional resource. Document review still requires human oversight, but professional indemnity insurance provides the confidence lawyers need to adopt AI at scale, much as it does for human output. Gradual implementation with proper risk coverage ensures reliability as capabilities expand responsibly. 

The AI sword is only as sharp as the wielder 

A recent MIT report highlighted the lacklustre return on investments for AI tools in enterprise settings. 95% of companies do not see gains from the AI products they pilot. It isn’t too different for lawyers. 

Much like the VR craze of 2020, the novelty of new technology wears off quickly when there isn’t a well-defined use case. A VR headset will keep you entertained for so many hours before it gathers dust in the corner. ChatGPT may have supplanted traditional search engines for many consumers, but despite 42% of large US enterprises reporting active AI deployment, only 28% of employed Americans use AI tools at work

Lawyers bill by the minute and lack time for extensive customisation. They need systems that integrate seamlessly without hours of training or prompt engineering. Real estate teams require AI that speaks their language and recognises their documents immediately. Generic tools force lawyers to become coders, defeating the efficiency promise entirely.  

Over a third of AI use within legal teams currently resides in general-purpose tools not designed for property law. These tools miss context, overlook document relationships, and erode trust in output. What was supposed to reduce effort ends up adding risk. 

Defining AI success in real estate 

Real estate’s spatial, regulatory, and financial complexity demands purpose-built AI solutions. Success requires three elements that general-purpose tools cannot deliver effectively. 

First, deep domain expertise must be embedded in the technology itself. Second, visual intelligence must process maps and spatial data as naturally as text. Third, implementation must respect how lawyers actually work. Real estate professionals need AI that understands land titles, survey notes, and covenant breaches without extensive prompting. They need tools that integrate with existing workflows and deliver value immediately.  

High-stakes practices demand specialized solutions. Real estate represents too large and complex an asset class for one-size-fits-all approaches. As the sector continues its digital transformation, purpose-built AI will separate thriving practices from those still drowning in manual review.